Attribution

The primary goal for anyone using marketing attribution today is to understand core advertising effects. Are you reaching the right people? Are your ads effective? Companies working toward this goal...

There is significant debate in the data science community around the most important ingredients for attaining accurate results from predictive models. Some claim that it’s all about the quality and/or quantity of data, that you need a certain size data set (typically large) of a particular quality (typically very good) in order to get meaningful outputs. Others focus more on the models themselves, debating the merits of different single models – deep learning, gradient boosting machine, Gaussian process, etc. – versus a combined approach like the Ensemble Method.

Marketing attribution is a powerful tool for evaluating ad tech vendors. Attribution helps you compare which vendors contributed to a conversion, and in what way: how many touchpoints did it take for a user to convert? How many impressions were garnered over how long a time period? How many net conversions did each vendor generate?

Last month, we released a comprehensive report along with IDG Connect analyzing how marketers are measuring performance. The report contains results from a survey we conducted of 250 marketing professionals, offering a real-world glimpse into what’s working and what’s not. We also hosted a webinar with IDG Connect’s Bob Johnson to get additional insight and color into our findings. You can listen to the complete recording here, but here are some of the highlights.

TV remains notoriously hard for advertisers to accurately attribute value to online conversions, mobile downloads and other digital actions. It has been predominantly considered an upper funnel or brand awareness channel. Unlike digital channels, often there is no traceable journey to conversion. That means that one of the most expensive channels is also the toughest to measure and quantify ROI.

Elsewhere in marketing, the user is front and centre. User experience, customer engagement, outside-in marketing – all are buzzwords that remind us that we’re selling to real people, not robots. Yet, an equal and opposite force is also at work. Alongside discussions of UX and CX, marketers drive towards increased automation, programmatic strategies, and measurement derived from mind-bendingly complex algorithms. Attribution typically operates in this latter paradigm.

Your media team negotiated a great deal for prime time spots in major channels. But you are not a direct response marketer with a “1800 number, order now” call to action, nor can you just include it in your branding efforts with less stringent ROI expectations. It would be great to have numbers to share with the SEM team to demonstrate that TV is also responsible for the spike in search. Those numbers don’t exist within your BI tool, website analytics tool, marketing automation/analytics tool or within silo-ed channel performance tools. The question your left with is how do I create accountability for my TV spend, understand channel lift and TV’s short and longer term impact on my business?

We are flooded with data. Each of the many tools in our martech, adtech, and overall technology stacks gives us a myriad of information to work with – and the number of tools that we use continues to grow. This is a blessing and a curse. We now have the data we need to understand precisely how our efforts perform. But without some parameters around that data, it’s hard to find the signal through the noise.

At last count – and this was almost a year ago – there were 3,874 marketing technology solutions on the market. I explored some of these in my last post, but it’s obviously impossible to sum up almost 4,000 tech offerings in one article. The breadth and scale of the relatively nascent MarTech industry is impressive, by equal turns inspiring and bewildering. What it’s not, at least to CMOs and other marketing leaders, is surprising. You know how much MarTech is out there, because there’s a good chance that you’ve been part of its explosion.

Digital marketers have always been under scrutiny for media efficiency. Marketers’ measurement approach is anywhere on the spectrum from last click to advanced predictive algorithmic measurement. Given the indisputably wrong metrics last click produces, many marketers have graduated to algorithmic measurement.

We all know that measuring marketing and advertising efforts isn’t easy. It never has been – insert overused John Wanamaker quote here. But the recent proliferation of channels and devices has made marketing measurement exponentially more complex than ever before. Technology helps, and has tried to keep up, but having so many ways to measure so many components can also feel daunting at best, downright overwhelming at worst.

There is some great discussion coming out of Australia in the wake of ADMA Data Day. It’s always fun to see what other industry experts and professionals are saying about the marketing attribution space, but even better when it’s something I so wholeheartedly agree with.

Marketing attribution is a relatively simple concept supported by relatively complex data science. The idea of attributing an action or conversion to a particular activity or source intuitively makes sense; it is, in fact, what pretty much every marketer worth his or her salt has been trying to do since the beginning of time. It’s the how that gets tricky.

I’m calling it: Black box attribution is officially on its way out. It’s no longer necessary to blindly trust that your attribution platform will magically turn data into meaningful insights. It’s time for the data, and the insights, to escape the black box and join their technology friends in the light.

We’ve all been there. You’re interested in a product. You go to their website, check it out. You like it, but you’re not ready to buy, or you get distracted. A few days later, an email reminds you that “you forgot something,” and a day after that, an ad pops up on your favorite blog for the exact item you were considering.

When marketing attribution hit the mainstream five years ago, it seemed like an answer to marketers’ prayers. Finally: a data-driven way to go beyond last-click models and truly give credit where credit is due across the cross-channel landscape. Attribution complements MMM (marketing mix modeling) with the power of big data and real-time analysis, providing insight into each touch of the conversion funnel in seconds instead of weeks. For direct response marketers, this is a slam dunk: in the post-800 number days, understanding purchase path to conversion is critical.

In December 2015, Scott Denne of 451 Research conducted an extensive, objective analysis of Conversion Logic. We gave Scott full access to our background, strategy, and products so that he could provide his expert opinion on our place in the market.

Every new technology experiences growing pains, and marketing attribution is no different. While the idea of attributing a conversion to a specific action, whether in a store or on the web, is as old as business itself, sophisticated solutions for measuring the interplay and impact of those actions are relatively new.

A strange thought struck me as I watched the Super Bowl yesterday: football and attribution have a lot in common. In both scenarios, a highly trained team of professionals executes a series of intricate plays in pursuit of a singular goal: conversions. When played well, football games and marketing campaigns connect to their respective audiences emotionally, and, more cynically, inspire them to open their wallets.